Alertness prediction system and method

ABSTRACT

An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a Continuation application of U.S. patentapplication Ser. No. 16/364,003, filed Mar. 25, 2019, which is aContinuation application of U.S. patent application Ser. No. 15/436,039,filed Feb. 17, 2017 entitled Alertness Prediction System and Method,which claims priority to U.S. Provisional application Ser. No.62/296,800 entitled Alertness Prediction Algorithm, filed on Feb. 18,2016 [Atty. Doc. No. TVC-141USP] and U.S. Provisional application Ser.No. 62/432,977 entitled Alertness Prediction System and Method, filed onDec. 12, 2016 [Atty. Doc. No. TVC-141USP1], the contents of eachincorporated fully herein by reference.

BACKGROUND OF THE INVENTION

The unchecked degradation of an individual's alertness is a growingconcern and the consequences in some areas are approaching epidemicproportions. As an example, it is estimated that 250,000 drivers per dayfall asleep at the wheel. Serious and fatal truck, bus, train andautomobile accidents are occurring at an alarming rate. Many injuriesand accidents in manufacturing plants are fatigue related. The purposeof monitoring alertness is to prevent these and other emergencysituations from happening rather than dealing with them after the fact.For instance, it is already too late to wake someone up after they havefallen asleep at the wheel.

Historically, algorithms for predicting or estimating an individual'salertness were based upon what is often referred to as a two processmodel. The two process model is made up of a circadian rhythm processand a sleep-wake homeostasis model. The circadian rhythm aspect of themodel is typically based solely on a standard time period (e.g., 23-25hours). The sleep-wake homeostasis model, on the other hand, istypically based solely on actigraphy determinations.

A weakness to the current form of the two process algorithmic model isthat it generalizes its prediction of alertness based upon data gatheredfrom a small sample set. In general, the algorithm suffers from a lackof personalization to the individual for which it is intended to beused.

SUMMARY OF THE INVENTION

Aspects of the invention aim to improve upon previous models ofpredicting fatigue and alertness levels by gathering data from theindividual being monitored to create a more accurate estimation of theindividual's alertness levels. An algorithm or bio-mathematical modelmay be incorporated into a wearable device to detect, predict and/orestimate an individual's alertness based upon a culmination ofsubjective and objective measures.

One algorithmic bio-mathematical model in accordance with an aspect ofthe invention involves a two-process algorithm incorporating asleep-wake homeostasis determination and a circadian rhythm estimation.The sleep-wake homeostasis aspect of the model may be improved by usingactigraphy measures, in addition to distal skin, ambient light, andheart rate measures, to improve the accuracy of the sleep and wakedeterminations for the individual. The circadian rhythm model of fatigueprediction and estimation may be improved by combining distal skin,heart rate and actigraphy data. This circadian rhythm estimate producesa more accurate model that is able to capture a user's mid-afternoonlull and evening increase in alertness levels. The sleep-wakehomeostasis and circadian rhythm models may also be combined withadditional objective and subjective measures as well as informationsupplied by the user to improve the accuracy of the estimation evenfurther.

Other bio-mathematical models in accordance with aspects of theinvention may generate fatigue scores that predict the alertness of anindividual using various metrics. The bio-mathematical models, anddevices, systems, and methods incorporating the bio-mathematical modelsdescribed herein, could be used in scenarios where the alertness of anindividual is of interest. The bio-mathematical model can reside on astand-alone device (such as a wearable device) as an application orwithin another software environment. Some, or all, of the metrics ofinterest could be gathered and fed through the bio-mathematical model toproduce an output that is correlated to an individual's alertness level.

Existing systems and algorithmic models that estimate or predict anindividual's alertness level may be trained to a sample set ofindividuals and contain little to no feedback for circadian rhythmestimation. This produces highly inaccurate models of an individual'sactual circadian rhythm and often misses predictions of known circadianevents (such as the mid-afternoon lull and evening wakefulness) due tothe generalized and simple sinusoids. The inventive devices, systems,and bio-mathematical models described herein, however, continue toimprove their accuracy as the models adapt to the individual's circadianrhythm. The proposed models may be personalized to an individual whereother systems are generalized to a sample set of data.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is best understood from the following detailed descriptionwhen read in connection with the accompanying drawings, with likeelements having the same reference numerals. When a plurality of similarelements are present, a single reference numeral may be assigned to theplurality of similar elements with a small letter designation referringto specific elements. When referring to the elements collectively or toa non-specific one or more of the elements, the small letter designationmay be dropped. This emphasizes that according to common practice, thevarious features of the drawings are not drawn to scale. On thecontrary, the dimensions of the various features are arbitrarilyexpanded or reduced for clarity. Included in the drawings are thefollowing figures:

FIG. 1 is a block diagram of a wearable device in accordance withaspects of the present invention;

FIG. 2 is a diagram depicting the interaction of sleep-wake homeostasis(the homeostatic sleep drive) with an individual's circadian rhythm inaccordance with aspects of the present invention;

FIG. 3 is a chart depicting exemplary events or features within anindividual's circadian rhythm, in accordance with aspects of the presentinvention;

FIG. 4 is a block diagram of a system including a wearable device asdescribed herein in communication with external devices in accordancewith aspects of the invention;

FIG. 5 is a flow chart of steps for predicting the alertness of a userin accordance with aspects of the invention;

FIG. 6 is a flow chart of an exemplary method for implementing conceptsaccording to FIG. 5 in accordance with aspects of the invention;

FIG. 7 is a diagram of an alertness prediction output from abio-mathematical model in accordance with aspects of the invention;

FIG. 8 is a flow chart of an exemplary method for estimating fatigue inaccordance with aspects of the invention;

FIG. 9A is a graph of a first coefficient that may be extracted in themethod of FIG. 8;

FIG. 9B is a graph of a second coefficient that may be extracted in themethod of FIG. 8;

FIG. 9C is a graph of a third coefficient that may be extracted in themethod of FIG. 8; and

FIG. 9D is a graph of a fourth coefficient that may be extracted in themethod of FIG. 8.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the invention provide a wearable device having abio-mathematical model which predicts fatigue levels for an individualusing various metrics. Certain aspects involve a two-process algorithm,a type of bio-mathematical model, which predicts alertness levels byusing accurate measures of actigraphy and estimations of an individual'scircadian rhythm. The wearable device may also be connected to or incommunication with other systems such as, for example, a smart phoneapplication or other “smart” devices. Both actigraphy and circadianrhythm estimations can be made using measurements of the individual'smovement, body position, heart rate, and distal skin temperature. Thealertness prediction of the bio-mathematical model can be furtherimproved in accuracy by including additional objective and subjectivemeasures, which are described in further detail herein. Thebio-mathematical model enables improvements due to closed loop feedbackand through continuous learning and monitoring.

FIG. 1 depicts a wearable device 100 for monitoring an individual'sfatigue and providing a prediction of the individual's alertness levels,e.g., to the individual wearing the device 100 and/or another entity. Asuitable wearable device is described in U.S. Utility application Ser.No. 14/848,771. The illustrated wearable device 100 is embodied in aband 102, which may be placed on the individual's wrist, for example.The band 102 supports at least one motion sensor 104 and at least onebiometric sensor module 105 for monitoring the individual's biometrics.The biometric sensor module 105 may include at least one of a skintemperature sensor 105 a or a heart rate monitor 105 b. Suitable motionsensors 104 and biometric sensor modules 105 for use with the presentinvention will be understood by one of skill in the art from thedescription herein.

The motion sensor 104 may include one or more gyroscopes and/oraccelerometers to track movements (linear, angular, etc.). The movementsmonitored or tracked may include prescribed motions of the user, othermovements by the user outside of prescribed motions, the user's relativemotion, or motion caused by the user's environment (such as vibrationfrom a truck engine, etc.). In addition to measuring movement, themotion sensor 104 may be used to estimate the user's body position (e.g.sitting, standing, lying down).

Techniques for tracking movements and/or body position are throughaccelerometers and/or gyroscopes. There are many small, low-powergyroscopes available on the market. The gyroscopes typically employpiezoelectric sensors or other forms of micro-electronic motion sensors(MEMS). For instance, SGS-Thompson Microelectronics (st.com) has a lineof MEMS based gyroscopes that operate on low power, measure all threeaxes of movement, provide digital output that can be fed directly into amicroprocessor, and that have a low noise threshold and low gyroscopicdrift, allowing them to measure the fine movements with high precisionand repeatability. The L3G3200D is a suitable device having anoperational voltage range from 2.4V to 3.6V, which is well suited forbattery operation, consumes only 6.1 mA in typical operation, has anoperating range of −40 to +85 degrees Celsius, includes an embeddedtemperature sensor, and has digital output of both temperature andangular rates of movement, with up to 16-bits of precision for angularrates.

As an alternative to a MEMS gyroscopes, linear accelerometers may beused. Since MEMS linear accelerometers respond to the gravitationalfield as well as linear acceleration, when arranged in a three-axisconfiguration, it is possible to compute rotational changes to yaw,pitch, and roll, as described in the paper “Tilt Sensing Using aThree-Axis Accelerometer,” by Mark Pedley; Freescale Semiconductor,Document Number AN3461, Revision 6, March 2013, which is incorporatedfully herein by reference.

The biometric sensor module 105 may include one or more sensors tomeasure one or more biomarkers of the user. Biomarkers that may bemeasured in accordance with aspects of this invention include, but arenot limited to, skin temperature and heart-related metrics, includingheart rate. The biometric sensor module 105 may be used for continualand/or periodic passive measurements of various biomarkers of a user,e.g., at a rate of one measurement per minute. In some embodiments, thebiometric sensor module 105 may be generic and may include bothbiometric sensors and non-biometric sensors (e.g., an ambient lightsensor 107). In an embodiment, the biometric sensor module 105 may beintegrated as a unit within the device 100. In another embodiment, thebiometric sensor module 105 may be comprised of several componentsdispersed within and/or throughout the device 100.

The biometric sensor module 105 may include a skin temperature sensor105 a and a heart rate sensor 105 b, such as the Pulse Rate Sensor fromKarisson Robotics. The skin temperature sensor 105 a may be used tomeasure the temperature of the user's skin at the location of thewearable device 100. Silicon Labs makes an Integrated circuit chip thatincludes a pulse rate sensor/heart rate sensor as well as blood oximetry(oxygen saturation of the blood). However, while these types of systemsmay be advantageous in determining whether the system was currentlybeing worn, just the temperature sensor may be employed in accordancewith some aspects if a design goal is to preserve battery life. Forexample, oximetry sensors that employ a light emitting diode and sensorto measure the oxygen saturation and have a high current draw may beomitted.

The biometric sensor module 105 may also be used to detect changes overtime in the user's various biomarkers, including heart-related metricsand skin temperature. The changes may be detected through continual andperiodic passive objective measurements of the user with the one or moresensors within the biometric sensor module 105.

In accordance with aspects of the invention, the wearable device 100 isembodied in a comfortable wrist band, similar to a watch. However, thedevice 100 could also work attached to the forearm, worn around theelbow, or attached to essentially any body part. Additionally, thedevice 100 may be incorporated into an article of clothing such as aglove or other means of holding it on the user. The design of the device100 in accordance with aspects of the invention is such that it is notobtrusive for an operator to wear, helping to ensure that the operatorwears it. Towards that end, the biometric sensor module 105 may be usedto detect whether the wearable device 100 is currently being worn (e.g.,based on a temperature measurement indicating it is currently againstthe user's skin). For example, temperature sensors and/or heart ratesensors would work for this purpose. Other biometric sensors of thebiometric sensor module 105 may be used for this purpose. The motionsensor 104 and any monitored motions can also be used to determinewhether the user is currently wearing the device 100.

The wearable device 100 has a memory 110 which stores a bio-mathematicalmodel for predicting an Individual's level of alertness or fatigue. Thebio-mathematical model may be a two-process algorithm, whichincorporates a sleep-wake homeostasis determination and a circadianrhythm estimation. Sleep-wake homeostasis reflects an individual's needor desire to sleep. The sleep-wake homeostasis determination (orhomeostatic sleep drive) may be composed of factors such as time sincethe user last slept (sleep debt), the length of the last sleepingsession of the user, and the quality of the sleep during the lastsleeping session of the user. Determining when the user is actuallyawake or asleep is accomplished using the method referred to asactigraphy. The sleep-wake homeostasis aspect of the model uses accurateactigraphy measures derived from the movements detected by the motionsensor 104, in addition to distal skin, ambient light and heart ratemeasures, to improve the accuracy of the sleep and wake determinationsfor the individual. The model also includes a circadian rhythm modelaspect of fatigue prediction and estimation which is derived bycombining distal skin, heart rate and actigraphy data. This circadianrhythm estimate is able to capture a user's mid-afternoon lull andevening increase in alertness levels.

The memory 110 also stores a generalized default estimation of circadianrhythm which is derived from a sample of a general population of people.The generalized default estimation assumes an approximate 24-hourcircadian rhythm cycle. When the individual first puts on the device100, the device 100 applies the generalized default estimation to theindividual. However, over time, the device 100 adjusts the generalizeddefault estimation to reflect the individual's actual circadian rhythmvia applying the stored bio-mathematical model, based on variouscontinual and passive measurements of the individual in a closed-loopsystem. The measurements may include movement, skin temperature, andheart rate. An individual's personal circadian rhythm may actually varybetween 23.5 and 25 hours, for example, deviating from the generalizeddefault estimation. Thus, the generalized default estimation isconfigured to be adjusted according to an estimation of an actualcircadian rhythm of the individual, thereby personalizing thepredictions of alertness for the individual after applying thebio-mathematical model. For example, an adjustment to the generalizeddefault estimation of the individual could be applied after theindividual wears the device 100 for two days, and the measurements overthe two days indicate that the generalized default estimation isinsufficient to reflect the actual circadian rhythm of the individual.

A processor 108 is coupled to the motion sensor 104 and the biometricsensor module 105. The processor 108 may be a programmablemicroprocessor. The processor 108 is also coupled to the memory 110 forstoring and retrieving data. The processor 108 may execute instructionsor apply the bio-mathematical model stored in memory 110 to provide thefunctionality of the wearable device 100 described herein. The processor108 may also store data retrieved from the motion sensor 104 andbiometric sensor module 105 in memory 110 and retrieve stored data fromthe memory 110 for processing. The memory 110 may be conventional memorysuch as, for example, static random access memory (RAM). The processor108 may be a conventional microprocessor such as a low power consumptionembedded processor. A reprogrammable microprocessor device may beemployed, which enables firmware upgrades. A suitable processor 108 isan Altera MAX7000A, which operates at 3.3V (an operating voltage rangecompatible with suitable gyroscopes).

Processor 108 may also be coupled to a clock 112 for monitoring timedand/or scheduled events and a transceiver 114 for transmitting signalsto and/or receiving signals from a remote location. The clock 112 may bean integrated circuit clock capable of measuring time (e.g., infractions of a second such as milliseconds, microseconds, etc.). Thetransceiver 114 may be, for example, a Bluetooth transmitter, e.g., toenable the wearable device 100 to notify a telematics device, remotecomputer system, computer application, and/or a smart phone applicationin the event of a notification. The components of the wearable device100 may be powered by a battery 116. Battery 116 may be a rechargeablebattery such as a lithium ion battery cell.

Processor 108 may monitor the temperature and motion outputs from themotion sensor 104 and the biometric sensor module 105 to determinewhether the device is being worn against the skin. The motion outputsfrom the motion sensor 104 may be used by the processor 108 to monitorthe motion of the wearable device 100. The processor 108 may beconfigured to look for angular motion whose velocity is between 0 dpsand 2,000 dps (degrees per second). The low end of the range eliminatessmall angular shifts due to vibration and the high end of the rangeeliminates large scale radial motion, such as from a turning truck. Theoperator's response times as well as recorded temperatures and times maybe stored in memory 110 so that, for example, a dispatcher can verify ata later point in time that the device was being properly worn in theevent that a telematic system is not available to communicate.

Device 100 additionally may include an ambient light detector 107. Theambient light detector 107 may be used to detect the user's exposure tolight. Exposure to light can affect an individual's circadian rhythm andadjust the individual's circadian clock. This may shift the individual'scircadian rhythm. The bio-mathematical model can incorporate informationacquired by the ambient light detector 107 into a prediction of futurechanges to an individual's circadian rhythm in response to theindividual's light exposure. The ambient light detector 107 may beconfigured to determine the user's exposure to blue wavelengths oflight, which may have an exaggerated effect on the individual'scircadian rhythm. The processor 108 may also be coupled to the ambientlight detector 107. Processor 108 may monitor and process the outputsmeasured by the motion sensor 104, the biometric sensor module 105, andthe ambient light detector 107.

The processor 108 may also monitor the temperature, heart rate, andmotion outputs from the motion sensor 104 and biometric sensor module105 to assess, using the bio-mathematical model stored in the memory110, the sleep-wake homeostasis of the individual, including theindividual's periods of sleep and wakefulness, by incorporatingmeasurements of motion into actigraphy determinations.

The detection of time sleeping and time since the user last slept can bedetermined by the processor 108 through analysis of actigraphy movementdata indicating the user's lack of movement (which would indicate timeduring sleep), combined with biomarkers such as heart rate and skintemperature. The processor may adjust and/or confirm the actigraphydeterminations using the measurements of the distal skin temperature andthe heart rate. For example, the processor 108 could apply measurementsof distal skin temperature to the actigraphy determination (with patternrecognition or other techniques) to confirm if an individual is asleepor awake. This could be done with a threshold, looking for a deviationfrom baseline data, or with a pattern of an increase of skin temperatureover a period of time. An increase in distal skin temperature has beenshown to correlate with an individual being asleep as well as a decreasein distal skin temperature correlating with the individual being awake.

In addition, the processor 108 may apply measures of ambient light fromthe ambient light sensor 107 as an additional input to an actigraphicalsleep or wakefulness determination, such as in cases where it is noteasily determined whether the individual is awake or asleep. Forexample, if it is not easily determined that the person is asleep orawake, but there is a large amount of ambient light present, theactigraphical output may be a prediction that the individual is awake.On the contrary, an absence of light might indicate that the individualis asleep.

The processor 108 may also apply determinations of body position andheart rate to actigraphy determinations to confirm and/or adjust them.Body position could be used similarly to ambient light in that the bodyposition may provide additional indication as to whether the user isasleep or awake. For example, if the user is standing or sitting theyare less likely to be asleep than if they are lying down. Heart rate,similar to skin temperature, has a pattern indicative of whether anindividual is sleeping or awake. The processor 108 could use thisadditional input to better improve the accuracy of the sleep/wakepredictions of the actigraphy to improve the sleep-wake homeostasisassessment.

The processor 108 also estimates the individual's circadian rhythm. SeeFIG. 2. The processor 108 may process initial measurements of at leastone biomarker, such as skin temperature or heart rate, to estimate theuser's personal circadian rhythm. The resulting processed data can becharted, for example, as in FIG. 2, and the processor 108 can identifyor extract features or events correlating to specific locations withinthe Individual's circadian rhythm. A user's alertness throughout the daycan be strongly correlated with the position of a user within the user'scircadian rhythm. The ability to estimate a user's circadian rhythm canprovide an accurate prediction of the user's alertness at any point in agiven day.

A biomarker for estimating a user's personal circadian rhythm is theuser's distal skin temperature. A user's distal skin temperature iscorrelated with the user's core body temperature. The core bodytemperature follows the user's circadian rhythm, and the core bodytemperature will increase during the hours of wakefulness and decreaseduring typical sleeping hours as a result of following the user'scircadian rhythm. The user's levels of alertness will therefore alsochange with the circadian rhythm. Because the user's body regulates corebody temperature by dissipating heat through the limbs of the body, thetemperature of the limbs increases when core body heat decreases.Therefore, the measurements of a user's distal skin temperature can beused to accurately estimate the user's personal circadian rhythm bycorrelating the distal skin temperature with core body temperature,which follows the circadian rhythm of the user. This provides a model ofalertness levels for the user.

Distal skin temperature may also be correlated with a user's melatoninlevels. A user's level of endogenous melatonin is a reliable andaccurate indicator of the user's location within the personal circadianrhythm and therefore an indicator of the user's degree of alertness.Melatonin typically rises during times of decreased alertness (e.g., theperiod before nightly sleep) and typically falls during times ofincreased alertness. Skin temperature generally correlates withmelatonin levels in that when melatonin levels increase, the skintemperature of the user also increases in connection with the user'scircadian rhythm. In this way, skin temperature may act as a correlativeproxy for determining the user's current levels of melatonin, andtherefore the user's current levels of alertness as determined by theuser's location within the personal circadian rhythm.

Initial measurements of a person's distal skin temperature forestimation of a user's personal circadian rhythm and/or melatonin levelsmay be taken at various locations on the user's body, including feet,arms, wrists, and hands. Other initial measurements of biomarkers thatmay be incorporated into the processor's 108 estimation of a user'spersonal circadian rhythm and/or melatonin level may include, but arenot limited to, heart-related metrics such as heart rate.

An example of estimating a user's personal circadian rhythm may beginwith the user wearing the wearable apparatus 100 at a distal location onthe body, such as the wrist, for a testing period. The testing periodmay have a duration of two or more days. Ambulatory skin temperaturesmay be measured by the temperature sensor 105 a at a frequency of onceper minute for the span of the at least two days. Based on the dataderived from the distal skin temperature measurements, the processor 108may estimate a user's personal circadian rhythm. Other initialmeasurements of biomarkers over the testing period may also be used toestimate the circadian rhythm of a user.

The circadian rhythm estimation by the processor 108 is enabled bygathering measurements of an individual's distal skin temperature and/ortheir heart rate for a period of time. The processor 108 generates datapoints derived from measurements of distal skin temperature and heartrate. The processor 108 may also incorporate measurements of movementsby the individual to refine the data points. The data points representpoints of time within the Individual's actual circadian rhythm, and theprocessor 108 can use these data points to estimate the individual'soverall circadian rhythm by compiling them over time as a progression.The processor 108 also uses these data points to adjust the generalizeddefault estimation stored in the memory 110 to better reflect theindividual's actual circadian rhythm. The processor 108 may applypattern recognition and/or machine learning techniques to effect theadjustment such that the circadian rhythm determination is personalizedto the individual.

An Individual's circadian rhythm typically does not change drasticallyfrom day to day. However, an individual's activities may change eachday. These activities can “mask” the indicators of an Individual'scircadian rhythm, which is typically stable. Because the distal skintemperature and/or heart rate are affected by other outside “masking”events (such as walking, sleeping, etc.), the processor 108 may have toapply additional signal processing techniques to separate, or “demask,”the skin temperature or heart rate data from these “masking”non-circadian rhythm events. The processor 108 applies a “demasking”algorithm to remove the “masking events” from the underlying skintemperature and heart rate data (i.e., raw circadian data) to provide anaccurate prediction of circadian rhythm. For example, the masking eventof a person periodically getting up and walking (for example, from oneoffice to another) does not happen at the exact same time every day.This means that data points gathered at the same time point each day(for example, at 12:05 pm every day) can be examined across multipledays with signal processing techniques employed which can remove theoutlying “masking” factors and retain the underlying consistent signal.This is most easily implemented with a technique of averaging each ofmultiple points in a given 24 hour period across multiple days; however,additional signal processing techniques such as filtering may be used.Similarly, this same principal may be applied to heart rate measureswhich are similarly affected by circadian rhythm but have historicallynot been usable because of masking effects.

Variables considered in “demasking” of distal skin measurements andheart rate measurements include body position and activity of theindividual, which are “masking events” that may distort the underlyingcircadian rhythm signals from skin temperature and heart rate. Forexample, the typical environment in which a circadian rhythm signal isobserved is in a laboratory setting where the user lays in a bed in afixed position, with minimal food intake and no movement. “Demasking” isthe process of removing the effects of events occurring outside such acontrolled environment. As an example, the individual may engage injogging. When this occurs, the Individual's distal skin temperature isreduced as the individual begins to sweat. Additionally, theIndividual's heart rate increases due to physical activity. Because ofthis, the underlying circadian signal is typically lost. However, theprocessor 108 applies a “demasking” algorithm which is able to preservethe underlying circadian signal in the presence of these masking effectsby incorporating historical information saved in the memory for aspecific time period and the situational information. If the device 100and the processor 108 know that the individual is running, the processor108 can determine that the data being received is bad data and can bediscarded, or its significance can be reduced in determining actualalertness via the bio-mathematical model.

In a preferred embodiment the circadian rhythm estimation is alsoimproved by a concept of a quality factor that is associated with eachdata point. If additional information can be known about the conditionin which a data was captured (for example if the user was walking orasleep) then this data point can be given a quality factor. For example,if a user is walking when a data point is captured then it would beconsidered a low quality data point, conversely if a user has beensitting for a period of time when a data point is captured, it would beconsidered a high quality data point. Using this concept of a qualityfactor, the accuracy of the circadian rhythm can be improved as all datapoints are not treated as equal. The processor 108 may “demask” or applya quality factor to skin temperature or heart rate data within a datapoint by averaging a given data point for a specific time period overseveral days. For example, if the distal skin temperature is gathered atexactly 12:05 PM on a given day, an individual may be running to catchthe bus, resulting in the data point being given a low quality factor.The next day an additional data point would be gathered at 12:05 PM.This time the user is sitting in a chair and so this data point would bea high quality data point. By applying a weighted average to these datapoints a more accurate “demasked” distal skin temperature or heart ratecan be obtained by the processor 108.

The processor 108 uses the quality factor as a coefficient for aweighted average for combining several days' worth of circadian rhythmdata for a given point in the day. For example, the processor 108 maytake a data point at 12:05 pm on Tuesday and 12:05 pm on Wednesday.Tuesday was assessed a quality factor of 0.1, while Wednesday had aquality factor of 0.9. The resulting weighted average would becalculated by the processor as (0.1*Tuesday_data+0.9*Wednesday_data).This provides a better estimate than simply averaging the data acrossseveral days, because the processor 108 is not treating all data pointsas equal value.

Additionally, the processor 108 can estimate the actual time period ofthe circadian rhythm (which, for a typical individual, is not exactly 24hours) by incorporating the trends in the skin temperature and/or heartrate. With the skin temperature and/or heart rate data allocated aquality factor and/or “demasked,” the processor 108 can then normalizethe data and assume the circadian rhythm is a phase shifted andcorrelated pattern. Additionally, the melatonin levels can be predictedby sharp increases in the normalized distal skin temperature data, whichcan be used as a marker for circadian rhythm coefficient (the phase ϕ)shift and period (τ) of an individual's circadian rhythm.

The processor 108 can also combine the sleep-wake homeostasisdeterminations with the actual circadian rhythm estimation according tothe bio-mathematical model to result in a prediction of alertness of theindividual. This results in a more accurate and personalized alertnessprediction for the individual. Sleep-wake homeostasis and circadianrhythm work together within the individual to result in the individual'sever-changing alertness. See FIG. 3. The circadian rhythm can then becombined with the sleep homeostasis information by the bio-mathematicalmodel to create an overall estimate of alertness. Each input to thebio-mathematical model may be combined using pattern recognition and/ormachine learning techniques. Some of these techniques include weightingone portion over another. The weighted portions of the bio-mathematicalmodel may be statically or dynamically defined. For example, the weightgiven to the circadian rhythm is based upon the estimated quality of thedata the processor 108 has gathered.

FIG. 4 depicts a system 400 including a wearable device 100 inaccordance with aspects of the invention. The wearable device 100 may bein communication with, for example, a smart device 450 and/or anexternal computing device 460. The smart device 450 may be a mobiledevice such as a smart phone. The external computing device 460 may be apersonal computer or the like. Data collected by the wearable device 100may be communicated to a smart device 450 and/or an external computingdevice 460. The smart device 450 and/or the external computing device460 may also communicate other information to the wearable device 100.

The smart device 450 and/or the external computing device 460 may haveapplications or other software modules that can display, store, and/orfurther process the data received from the wearable device 100. Forexample, the smart device 450 may have a software application thatdisplays charts of an individual's alertness predictions or fatiguescores over time, derived from data received from the wearable device100 and stored by the smart device 450. The smart device 450 and/or thecomputing device 460 can also be used to alert an individual if the datareceived from the wearable device 100 predicts fatigue for theindividual. Additionally, the smart device 450 and/or the computingdevice 460 can communicate and exchange data with a data cloud 470.Thus, data received by the smart device 450 and/or the computing device460 can be transferred for storage to the cloud 470, and the smartdevice 450 and/or the computing device 460 can, for example, retrievethe data stored in the cloud 470 to generate charts or diagrams of anindividual's fatigue-related data over time. In addition, a third party,such as a manager or dispatcher, may be able to view informationregarding the individual's fatigue or alertness via the smart device 450and/or the computing device 460.

FIG. 5 depicts steps for predicting alertness of an individual inaccordance with aspects of the invention. First, at step 500, motiondata produced by a motion sensor, distal skin temperature data producedby a temperature sensor, and/or heart rate data produced by a heart ratemonitor may be obtained or received by a processor 108. Each of themotion sensor, the temperature sensor, and the heart rate monitor may beassociated with a wearable device 100 worn by the individual. The motionsensor may also produce data on the individual's body position or dataon the types of movements performed by the individual at step 500 a,which may also be obtained by the processor 108. Step 500 a may alsoinclude ambient light data produced by an ambient light sensor beingobtained or received by the processor 108.

At step 500 b, the processor may process the distal skin temperaturedata and/or the heart rate data to refine the data as directed by abio-mathematical model, which may be stored in a memory of the wearabledevice 100. The processor 108 may apply signal processing techniques,including, for example, low-pass filtering and moving averages, toeffect the processing of the skin temperature data and heart rate data.Such processing/filtering removes “noise” from the distal skintemperature data signal and/or the heart rate data signal to produce acleaner, more accurate signal.

At step 500 c, the processor may allocate a quality factor to the skintemperature data and/or the heart rate data based on at least one of thedata on the Individual's body position and the types of movementsperformed by the individual in accordance with the bio-mathematicalmodel.

At step 500 d, the processor may identify circadian and non-circadiandata within the skin temperature data and/or the heart rate data (i.e.,raw circadian data), and remove the non-circadian data to obtain refinedcircadian data (i.e., “demasking” the circadian data). Circadian data isdefined as data derived from circadian rhythm events, whilenon-circadian data is data derived from non-circadian events. Theprocessor may remove the non-circadian data using pattern recognitionand/or machine learning techniques. The processor 108 may also detectlocal maximum events and local minimum events within therefined/demasked circadian data to identify potential times of fatiguerisk for the individual.

At step 510, the processor 108 may make actigraphy determinations usingthe motion data it received from the motion sensor. The processor 108may then refine the actigraphy determinations at step 510 a using atleast one of the distal skin temperature or the heart rate data to makemore accurate actigraphy determinations.

The processor 108 may then use the actigraphy determinations at step 520to assess the individual's sleep-wake homeostasis bio-mathematicalmodel, including periods of sleep and wakefulness of the individual.This assessment may occur repeatedly. The processor may use either theraw actigraphy determinations or the refined actigraphy determinationsto assess the individual's sleep-wake homeostasis. Additionally, theprocessor 108 may incorporate data on the individual's body position torefine the sleep-wake homeostasis assessments. The processor may alsorefine the sleep-wake homeostasis assessments of the individual byincorporating the ambient light data.

At step 530, the processor 108 may calculate data points using at leastone of skin temperature data or the heart rate data. The processor 108may incorporate the processed of skin temperature and/or the heart ratedata from step 500 b, or unprocessed data to calculate the data points.Additionally, the processor 108 may incorporate the quality factorallocated to the skin temperature data and/or the heart rate data fromstep 500 c into the calculation of the data points. The processor 108may also incorporate the skin temperature data and/or the heart ratedata with or without the non-circadian data removed to calculate thedata points.

At step 540, the processor 108 may generate an estimated circadianrhythm for the individual. This may occur periodically. The processor108 may generate the estimated circadian rhythm by using the processeddata points to refine a default circadian rhythm stored in the memory ofthe wearable device. The default circadian rhythm may be derived from asample of a general population of people, and the default circadianrhythm may assume an approximate 24-hour circadian rhythm cycle.Additionally, the processor 108 may refine the estimated circadianrhythm by incorporating the ambient light data.

The processor 108 may also estimate the individual's circadian rhythmcoefficient (current phase ϕ), the individual's wake/sleep coefficient,the individual's circadian rhythm period (τ), the individual's sleeponset time, and/or the individual's melatonin onset at step 540 a. Eachindividual may have a different circadian rhythm coefficient/phase (ϕ)shift, which means each individual's circadian rhythm period (τ) maystart at different times. The sleep onset time can be determined by, forexample, identifying low points within demasked distal skin temperatureof an individual, followed by an increase (for example, a 35% Increase)in the demasked distal skin temperature. Low points correlate with highlevels of alertness within the individual, while a 35% increase from alow point indicates melatonin onset. Melatonin onset can be used, inturn, as a marker for the time at which an individual's circadian rhythmcycle or period (τ) begins.

At step 550, the individual may enter objective and subjectiveparameters into the device 100. The individual may also enter thesubjective and objective parameters on a smart device 450 and/or anexternal computing device 460, such that the parameters may becommunicated to and used by the wearable device 100. Parameters that maybe entered include, but are not limited to, prescribed motions asdescribed in detail by U.S. Utility application Ser. No. 14/848,771,data regarding the individual's medical history, susceptibility to theeffects of not getting enough sleep, data from questionnaires answeredby the individual, and subjective assessments by the individual of hisor her own levels of alertness.

At step 560, the processor 108 may combine the sleep-wake homeostasisassessments with the estimated circadian rhythm with thebio-mathematical model to predict an individual's level of alertness orto generate a fatigue prediction. The processor 108 may incorporate thesubjective and objective parameters to further refine fatiguepredictions for the individual. These parameters may be weighted in anon-linear manner using pattern recognition or machine learningtechniques to incorporate them into the refinement of the prediction inaccordance with a bio-mathematical model. The processor 108 may also usethe estimated circadian rhythm coefficient/phase (ϕ), circadian rhythmperiod (τ), wake/sleep coefficient, sleep onset time, and/or melatoninonset to predict the individual's alertness. The processor 108 may alsoincorporate either refined or unrefined sleep-wake homeostasisassessments and/or a refined or unrefined estimated circadian rhythmwhen making the prediction according to a bio-mathematical model. Theprocessor 108 may also refine the prediction of alertness for theindividual by using the detected local maximum events and the localminimum events. The prediction of alertness may thereafter becommunicated by the wearable device 100 to an external computing device460 and/or a smart device 450 for display, storage, and/or furtherprocessing.

FIG. 6 depicts steps of an exemplary method 600 for implementing theconcepts according to FIG. 5. First, at step 602, data on anindividual's movements, distal skin temperature, and heart rate may beobtained. A wearable device 100, or a processor 108 of the wearabledevice may obtain this data as signals from a motion sensor 104, atemperature sensor 105 a, and/or a heart rate monitor 105 b. Thewearable device 100 or the processor 108 may also receive signals fromthe motion sensor 104 indicating the individual's body position.

At step 604, signals received from the temperature sensor 105 a and theheart rate monitor 105 b may be processed by the processor 108 to cleanup the data as directed by the bio-mathematical model, which is, forthis exemplary method 600, a two-process algorithm. The processor 108may apply low-pass filtering and moving averages to improve the signalprocessing of the skin temperature and heart rate data.

At step 606, the skin temperature and heart rate data may be “demasked”in accordance with the two-process algorithm so as to remove obscuringsignals caused by non-circadian events from the underlying measured datasignals of distal skin temperature and heart rate of the individual.These events include, but are not limited to, sleep, physicalactivities, and certain body positions. The underlying signals relatedto circadian rhythm may be “demasked,” for example, by averaging severaldays' worth of data together. The “demasked” data, including skintemperature heart rate data can then be used to generate data points forthe individual's actual circadian rhythm. A quality factor may also beallocated at step 606 to skin temperature and/or heart rate data basedon the type of detected movements made by the individual.

At step 608, the feature extraction aspect of the two-process algorithmis employed to extract meaningful events or circadian rhythm-relatedfeatures from the measured and “demasked” signals. These meaningfulevents or features may include slow increases in distal skin temperaturewhich follow the individual's circadian rhythm and indicate decreasinglevels of alertness. Also, sudden increases in distal temperature mayindicate sudden changes in alertness levels. Machine learning and/orpattern recognition techniques can be used to extract the events and/orpatterns. Several commonly used functions may be employed by thealgorithm to perform the feature extraction, including peak detectionalgorithms, interpolation between points, and the cosinor function.

At step 610, sleep onset time may be determined from the “demasked”data, which then may be used to estimate the current phase/circadianrhythm coefficient (ϕ) or location the individual is in within theindividual's circadian rhythm period (τ). Each individual may have adifferent phase (ϕ) shift, which means each individual's circadianrhythm period (τ) may start at different times. The sleep onset time canbe determined by identifying low points within the circadian rhythm,followed by a, for example, 35% Increase in the circadian rhythm. Lowpoints correlate with high levels of alertness within the individual,while a 35% increase from a low point indicates melatonin onset.Melatonin onset can be used, in turn, as a marker for the time at whichan individual's circadian rhythm cycle or period (τ) begins.

At step 612, local maximum and minimum points or events within“demasked” skin temperature data and/or heart rate data can be detectedand identified as potential times of fatigue risk for a givenindividual. These detected events may correlate with increased levels ofdrowsiness. For example, an increase in skin temperature around the 2:00pm-4:00 pm timeframe may be identified as a decrease in alertness,something that is often observed in the mid-afternoon hours.

At step 614, actigraphy data from the individual can be used todetermine the Individual's sleep and wakefulness periods, and theindividual's resulting sleep-wake homeostasis for the two-processalgorithmic model. This can be determined solely using the detectedmovements made by the individual. However, other measurements, such asheart rate, distal skin temperature, and ambient light exposure can beincorporated to make the determination of the Individual's sleep andactivity periods more accurate.

At step 616, the individual can input other subjective and objectiveparameters into the device 100. The individual may also enter thesubjective and objective parameters on a smart device 450 and/or anexternal computing device 460, such that the parameters may becommunicated to and used by the wearable device 100. These parameterscan be used to further refine predictions of alertness within theindividual. Parameters that may be input include, but are not limitedto, prescribed motions as described in detail by U.S. Utilityapplication Ser. No. 14/848,771, data regarding the individual's medicalhistory, susceptibility to the effects of not getting enough sleep, datafrom questionnaires answered by the individual, and subjectiveassessments by the individual of his or her own levels of alertness.These parameters may be weighted in a non-linear manner using patternrecognition or machine learning techniques to incorporate them into arefinement of the two-process algorithmic model.

At step 618, the inputs from the circadian rhythm aspect, including theperiod (τ), phase/circadian rhythm coefficient (ϕ), event features, andmelatonin onset derived from the data points are combined with the inputof the sleep-wake homeostasis aspect of the two-process algorithm toproduce a performance metric that predicts the alertness levels of theindividual. Each of these inputs to the algorithm is combined usingpattern recognition techniques and/or machine learning techniques. Someof these techniques may include weighting one portion of the algorithmover another. For example, the weight allocated to the circadian rhythmaspect of the algorithmic model is based upon the estimated quality ofthe data gathered. This alertness level prediction can be furtherrefined using the inputs derived from step 616, the other objective andsubjective parameters. These objective and subjective parameters mayalso be weighted prior to incorporation into the prediction of theIndividual's alertness.

FIG. 7 depicts an alertness prediction output for an individual over a24-hour period, derived from the bio-mathematical model. The broken linerepresents the individual's alertness risk on a scale from 0 to 10,wherein 10 represents the highest fatigue risk and serves as the fatiguerisk baseline. The diagram displays the individual's alertnessprediction changing over time throughout the 24-hour period. The diagramalso displays a period of sleep for the individual and depicts alertnesspredictions for the individual that indicate a low, medium, and highfatigue risk.

FIG. 8 depicts steps of a method 800 for estimating fatigue of awearer/individual in accordance with aspects of the invention. One ormore of the steps of method 800 may be omitted and/or repeated and/orperformed in order (including simultaneously) that may vary from thosedisclosed herein without deviating from the scope and spirit of thepresent invention.

At step 802, sensor data is obtained. The sensor data may includeinformation about the wearer, for example, movements, position, distalskin temperature, and/or heart rate. Additionally, the sensor data mayinclude environmental conditions, for example, ambient light levelsand/or temperature. A wearable device 100, or a processor 108 of thewearable device, may obtain the sensor data as signals from, forexample, a motion sensor 104, a temperature sensor 105 a, a heart ratemonitor 105 b, and/or a light sensor.

At step 804, signal processing is performed. For example, signalsreceived from the temperature sensor 105 a and the heart rate monitor105 b may be processed by the processor 108 to clean up the data asdirected by the bio-mathematical models described herein. The processor108 may, for example, apply low-pass filtering and moving averages toimprove the quality of the signal for the skin temperature and heartrate data.

At step 806, circadian rhythm is estimated from the received andprocessed signals. The processor 108 may identify circadian andnon-circadian data within the skin temperature data and/or the heartrate data (i.e., raw circadian data), and remove the non-circadian datato obtain refined circadian data (i.e., “demasking” the circadian data).The processor 108 may remove the non-circadian data using patternrecognition and/or machine learning techniques.

At step 808, individual parameters are obtained. The individualparameters may include subjective and/or objective parameters.Individual parameters may include, but are not limited to, dataregarding the Individual's medical history, susceptibility to theeffects of not getting enough sleep, ability to perform prescribedmotions, data from questionnaires answered by the individual, andsubjective assessments by the individual of his or her own levels ofalertness. The Individual parameters can be received from the wearervia, for example, a user input of the device 100 or a smart device 450and/or external computing device 460, such that the parameters may becommunicated to and used by the wearable device 100. These parameterscan be used to further refine predictions of alertness for theindividual.

At step 810, features are extracted from the received and processedsignals, the estimated circadian rhythm, and the obtained individualparameters. The extracted features may include markers indicating theshape of the circadian rhythm, such as local circadian highs and lows(e.g., the “post-lunch dip”); information regarding the user's sleephabits both on working days and free days, such as sleep latency, sleepinertia, circadian lows, circadian preference (morning vs. eveningperson), habitual sleep opportunity and location (phase), average sleeptime, and napping habits; general medical information, such as age, sex,BMI, etc. In one embodiment, the extracted features are (1.) on a workday: wake time, alarm use, energy dip time, bed time, mid-sleep time,sleep duration, sleep latency; (2.) on a free day: wake time, energy diptime, bed time, mid-sleep time, sleep duration, sleep latency; (3.) age;(4.) sex; (5.) BMI; and/or (6.) corrected mid-sleep phase information.The features may be extracted by simple averaging, peak and valleydetection (In a signal) before and after transforming the data (viaderivative, integral, phase shift, etc.), algebraic combinations,transformation based on a mapping function, transformation of data intofrequency domain, etc.

At step 812, one or more pattern recognition and/or machine learningalgorithms are applied to the extracted features to identify how theextracted features can be used to determine coefficients. The processor108 may apply pattern recognition and/or machine learning techniques topersonalize circadian rhythm factors to the individual and/or to weightone factor over another. The weighted portions of the algorithmic modelmay be statically or dynamically defined. The pattern recognition and/ormachine learning algorithms may apply peak detection algorithms,interpolation between points, and/or the cosinor function. In oneembodiment, a regression-based machine learning algorithm is applied tothe extracted features to determine the coefficients to be extracted inthe next step.

At step 814, coefficients are extracted, e.g., by processor 108. In oneembodiment, four coefficients are extracted. The four coefficients mayinclude a circadian rhythm coefficient (phase ϕ), a wake/sleepcoefficient, a circadian rhythm weighting coefficient, and a wake/sleepweighting coefficient. The processor 108 may extract the coefficients bytransforming the output (applying a mapping, phase shift, etc.) of themachine learning of step 812 so that it can be fed into thebio-mathematical model of step 818 described below.

At step 816, actigraphy data is determined. The processor 108 may makeactigraphy determinations using the processed motion data received fromthe motion sensor. The processor 108 may then refine the actigraphydeterminations using at least one of distal skin temperature and/orheart rate data to make more accurate actigraphy determinations, e.g.,is the person awake or asleep, is the person seated or moving, etc.

At step 818, a bio-mathematical model is applied, e.g., by processor108, to the extracted coefficients and determined actigraphy data. Inone embodiment, the bio-mathematical model includes at least twosub-models, e.g., an awake sub-model that is applied when an individualis awake and an asleep sub-model that is applied when an individual isasleep. Whether the individual is awake or asleep may be determined bythe processor 108 based on the actigraphy data, and the appropriatemodel is applied based on the determined awake/asleep condition.

At step 820, a fatigue score is generated. The fatigue score may begenerated by processor 108. The fatigue score, or an indication thereof,may be presented to the user or a person of interest (e.g., anemployer). If the fatigue score indicates a high level of fatigue,stimulus may be presented to the user (e.g., a vibration by the wearabledevice.

FIG. 9A depicts first coefficient values for 16 individuals. Thedepicted first coefficient values are circadian cycle/phase (ϕ) values.The optimal value for a coefficient is represented by a “o” and theextracted value determined by the coefficient extractor for thecircadian cycle is represented by an “x”.

FIG. 9B depicts second coefficient values for 16 individuals. Thedepicted second coefficient values are wake/sleep cycle values. Theoptimal value for a coefficient is represented by a “o” and theextracted value determined by the coefficient extractor for thewake/sleep cycle is represented by an “x”.

FIG. 9C depicts third coefficient values for 16 individuals. Thedepicted third coefficient values are circadian cycle weighting values.The optimal value for a coefficient is represented by a “o” and theextracted value determined by the coefficient extractor for thecircadian cycle weight is represented by an “x”.

FIG. 9D depicts fourth coefficient values for 16 individuals. Thedepicted fourth coefficient values are wake/sleep cycle weightingvalues. The optimal value for a coefficient is represented by a “o” andthe extracted value determined by the coefficient extractor for thewake/sleep cycle weight is represented by an “x”.

The information depicted in FIGS. 9A-9D illustrate the featuresextracted using techniques in accordance with aspects of the inventionare accurate at the individual level—enabling accurate prediction of anindividual's fatigue.

Although the invention is illustrated and described herein withreference to specific embodiments, the invention is not intended to belimited to the details shown. Rather, various modifications may be madein the details within the scope and range of equivalents of the claimsand without departing from the invention.

What is claimed:
 1. A device for monitoring and predicting alertness ofan individual, the device comprising: one or more sensors configured toobtain information signals about the individual, the sensors comprisingat least one of a motion sensor, a temperature sensor, and a heart ratemonitor; a memory configured to store: a default circadian rhythmconfigured to be refined with data derived from the information signalsabout the individual to generate an estimated circadian rhythm for theindividual, and a bio-mathematical model configured to generate afatigue score for the individual; and a processor coupled to the one ormore sensors and to the memory, configured to: receive the informationsignals about the individual, estimate a circadian rhythm of theindividual by incorporating the information signals about the individualto refine the default circadian rhythm, extract features from theinformation signals and the estimated circadian rhythm, extract at leastone coefficient from the extracted features, apply the bio-mathematicalmodel to the at least one extracted coefficient, and generate thefatigue score for the individual from the at least one extractedcoefficient using the bio-mathematical model.
 2. The wearable device ofclaim 1, wherein the bio-mathematical model is a two-process algorithmconfigured to predict alertness levels of the individual usingassessments of sleep-wake homeostasis and the estimated circadianrhythm; and wherein the processor is further configured to: makeactigraphy determinations using movement data or body position data,assess, using the two-process algorithm, the sleep-wake homeostasis ofthe individual, including periods of sleep and wakefulness, based on theactigraphy determinations, and combine the sleep-wake homeostasisassessments with the estimated circadian rhythm according to thetwo-process algorithm into the generation of the fatigue score for theindividual.
 3. The wearable device of claim 2, wherein the processor isfurther configured to: refine the actigraphy determinations using atleast one of a distal skin temperature data or a heart rate data of theindividual, and assess the sleep-wake homeostasis of the individualfurther based on the refined actigraphy determinations.
 4. The wearabledevice of claim 1, wherein the at least one coefficient includes atleast one of a circadian rhythm coefficient (Φ), a wake/sleepcoefficient (τ), a circadian rhythm weighting coefficient, or awake/sleep weighting coefficient.
 5. The wearable device of claim 1,wherein the processor is further configured to: process the informationsignals about the individual by using signal processing techniques;incorporate the processed information signals into the estimation of thecircadian rhythm of the individual; and incorporate the processedinformation signals into the extraction of the features.
 6. The wearabledevice of claim 1, wherein the processor is further configured to:obtain information signals about movement or position of the individual;determine actigraphy data for the individual using the informationsignals about the movement of the individual; apply the bio-mathematicalmodel to the actigraphy data, wherein the bio-mathematical model ischosen from one of an awake bio-mathematical sub-model and an asleepbio-mathematical sub-model; and incorporate the actigraphy data into thegeneration of the fatigue score for the individual.
 7. The wearabledevice of claim 6, wherein the processor is further configured to refinethe actigraphy data using at least one of the information signals abouta distal skin temperature or a heart rate of the individual.
 8. Thewearable device of claim 1, wherein the extracted features include oneor more of: markers indicating a shape of a circadian rhythm of theindividual, information regarding sleep habits of the individual on bothworking days and free days, and general medical information about theindividual.
 9. The wearable device of claim 1, wherein the processor isfurther configured to incorporate individual parameters about theindividual into the extraction of the features when the individualparameters are entered into the wearable device.
 10. The wearable deviceof claim 1, wherein the processor is further configured to: identify rawcircadian data within the information signals about the individual;identify non-circadian data caused by non-circadian events within theraw circadian data; remove the non-circadian data from the raw circadiandata to obtain refined circadian data; and incorporate the refinedcircadian data into the estimation of the circadian rhythm of theindividual.
 11. The wearable device of claim 1, wherein the sensors ofthe wearable device are configured to collect environmental informationsignals about the individual's environment, including at least one ofambient light level data or ambient temperature data, and whereinprocessor is further configured to incorporate the environmentalinformation signals into the estimation of the circadian rhythm.
 12. Thewearable device of claim 11, wherein: the sensors of the device furthercomprise an ambient light sensor configured to produce ambient lightdata from ambient light surrounding the individual, the processor iscoupled to the ambient light sensor, and wherein the processor isfurther configured to refine the circadian rhythm estimation with thebio-mathematical model by incorporating the ambient light data.
 13. Thewearable device of claim 11, wherein the temperature sensor is furtherconfigured to produce ambient temperature data from the ambienttemperature surrounding the individual, and wherein the processor isfurther configured to refine the circadian rhythm estimation with thebio-mathematical model by incorporating the ambient temperature data.14. A method for producing a fatigue score for an individual, the methodcomprising: obtaining, with sensors of a wearable device, informationsignals about the individual; estimating, with a processor of thewearable device, a circadian rhythm of the individual from theinformation signals about the individual; extracting, with theprocessor, features from the information signals and the estimatedcircadian rhythm; extracting, with the processor, at least onecoefficient from the extracted features; applying, with the processor, abio-mathematical model to the extracted coefficients; and generating,with the processor and using the bio-mathematical model, a fatigue scorefor the individual from the extracted coefficients.
 15. The method ofclaim 14, wherein the method further comprises: processing, with theprocessor, the information signals about the individual, by using signalprocessing techniques; incorporating, with the processor, the processedinformation signals into the estimation of the circadian rhythm of theindividual; and incorporating, with the processor, the processedinformation signals into the extraction of the features.
 16. The methodof claim 14, wherein the sensors of the wearable device are configuredto: collect environmental information signals about the individual'senvironment, including at least one of ambient light levels ortemperature; and incorporate, with the processor, the environmentalinformation signals into the estimation of the circadian rhythm of theindividual.
 17. The method of claim 14, wherein the method furthercomprises: identifying, with the processor, raw circadian data withinthe information signals about the individual; identifying, with theprocessor, non-circadian data caused by non-circadian events, within theraw circadian data; removing, with the processor, the non-circadian datafrom the raw circadian data to obtain refined circadian data; andincorporating, with the processor, the refined circadian data into theestimation of the circadian rhythm of the individual.
 18. The method ofclaim 14, wherein the method further comprises entering, into thewearable device, individual parameters about the individual; andincorporating, with the processor, the individual parameters into theextraction of the features.
 19. The method of claim 14, wherein the atleast one coefficient includes at least one of a circadian rhythmcoefficient, a wake/sleep coefficient, a circadian rhythm weightingcoefficient, or a wake/sleep weighting coefficient.
 20. The method ofclaim 14, wherein the method further comprises: obtaining, with a motionsensor of the wearable device, information signals about movement orposition of the individual; determining, with the processor, actigraphydata for the individual using the information signals about the movementof the individual; applying, with the processor, the bio-mathematicalmodel to the actigraphy data, wherein the bio-mathematical model ischosen from one of an awake bio-mathematical sub-model and an asleepbio-mathematical sub-model; and incorporating, with the processor, theactigraphy data into the generation of the fatigue score for theindividual.